Methods and systems for predicting a fault

ABSTRACT

Disclosed is a fault prediction system and method that uses non-fatal event data received from a terminal to make predictions concerning future fatal faults for the terminal. A complex fault pattern associated with a fault is applied to the non-fatal event data to predict the fault. A corrective action is provided for each predicted fault and historical data is used to predict a time to the predicted fault to govern the type of service response to create to prevent the fault.

RELATED APPLICATION

The present application is related to U.S. patent application Ser. No.______, concurrently filed with this application and entitled “METHODSAND SYSTEMS FOR SCHEDULING A PREDICTED FAULT SERVICE CALL.” The presentapplication and the related application are commonly assigned andincorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to a method and system thatstores information on a plurality of devices and predicts future devicefailures enabling corrective actions to be taken prior to a failure.

BACKGROUND

Any discussion of prior art throughout the specification should in noway be considered as an admission that such prior art is widely known orforms part of common general knowledge in the field.

Companies are increasingly relying on self-service terminals to providetheir customers with goods or services. In some cases, the self-serviceterminals replace employee operated terminals or employee providedservices. The self-service terminals are typically available for use bycustomers 24 hours a day and 7 days a week. Examples include automaticteller machines (ATMs), kiosks (e.g., DVD rental, airport and hotelcheck in, etc.), and self-service point-of-sale terminals.

A customer's level of satisfaction with a company can be directlyrelated to the availability of self-service and assisted serviceterminals used by the company to provide products and/or services to thecustomer. Therefore, a failure in one or more of these terminals thatrenders it unavailable to a customer and result in missed sales, missedopportunities to provide services, lower customer satisfaction and lossof customers.

What is needed is a system that reliably predicts a failure in aself-service or assisted service terminal and enables a correctiveaction to be taken prior to the failure.

SUMMARY OF THE INVENTION

It is an object of the present invention to overcome or ameliorate atleast one of the disadvantages of the prior art, or to provide a usefulalternative.

Among its several aspects, one embodiment of the present inventionrecognizes the need to increase the availability of a terminal bypredicting a fatal fault in the terminal and based on the predictedfatal fault, service the terminal to prevent the fault and thus increasethe availability of the terminal. Event data is used to predict a fault.Event data is received from one or more terminals and stored in adatabase. The event data includes events for one or more devices thatcomprise a terminal. Each predictable fault includes a complex predictedfault pattern that includes two specific fault patterns that apply to acertain type of non-fatal event data associated with a predictable faultfor one of the one or more devices. The event data must satisfy therequirements of both of the specific fault patterns before a fault ispredicted.

In another embodiment, life limit tally data is used to determine when adevice is operating beyond its expected usable life. In this mode ofoperation, predicted fault patterns are still used to predict a fault;however, the predicted fault patterns are adjusted to detect a faultusing lower values for the specific fault patterns.

In accordance with an embodiment of the present invention, a method forpredicting a fatal fault in a terminal using non-fatal event data ispresented. The method comprises the steps of: receiving a plurality ofevent data from the terminal where the plurality of event data includesnon-fatal event data; identifying non-fatal event data associated with afault from the plurality of event data; comparing the non-fatal eventdata to a predetermined fault pattern associated with the fault; andgenerating a fault predicted signal for the terminal when thepredetermined fault pattern is identified within the non-fatal eventdata.

A more complete understanding of the present invention, as well asfurther features and advantages of the invention, will be apparent fromthe following Detailed Description and the accompanying Drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The aspects of the claimed invention can be better understood withreference to the Drawings and the Detailed Description. The Drawings arenot necessarily drawn to scale. Throughout the Drawings, like elementnumbers are used to describe the same parts throughout the variousdrawing, figures and charts.

FIG. 1 is a high-level drawing in block form illustrating components ofan exemplar embodiment of a predictive services system.

FIG. 2 is a high-level drawing in block form illustrating components ofan exemplar embodiment of a database computer server.

FIG. 3A is a high-level block diagram illustrating an exemplarembodiment of event and tally records stored in a raw terminal databasetable.

FIG. 3B is a high-level block diagram illustrating an exemplarembodiment of certain event and tally records stored in a workingterminal database table.

FIG. 4 is a graph illustrating different types of service calls.

FIGS. 5A, 5B and 5C are high-level flow charts illustrating parts of anexemplar method for predicting a fatal fault.

FIG. 6 is a high-level flow chart illustrating an exemplar method forscheduling a predictive service call.

FIG. 7 is a high-level flow chart illustrating an exemplar method forscheduling a predicted action.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to providean understanding of the claimed invention. However, it will beunderstood by those skilled in the art that the claimed invention may bepracticed without these details and that numerous variations ormodifications from the described embodiments are possible.

Methods and systems are disclosed herein for performing a predictiveservice. As used herein, a predictive service includes a serviceprovided to a company where a plurality of terminals are monitored,event data from the monitored terminals is received and stored in adatabase, stored non-fatal event data is analyzed to predict a fatalfault in a terminal, and corrective action is scheduled to occur priorto the failure.

FIG. 1 illustrates one embodiment a data processing system forimplementing a predictive service system 100. In this embodiment, apredictive service is provided to a customer where the service monitorsa number of remotely located terminals 110 _(1-N). The terminals 110_(1-N) are located at one or more customer locations 105. Each of theterminals 110 _(1-N) includes a terminal data collector 115 _(1-N). Eachterminal data collector 115 _(1-N) collects local event data and tallydata (if available) and transmits the collected data to a terminal dataagent 125. In this embodiment, the terminals 110 _(1-N) are ATMs. EachATM terminal includes a magnetic stripe card reader, a currencydispenser and printer. In other embodiments, the terminals 110 _(1-N)are self-service checkout terminals. In still other embodiments, theterminals 110 _(1-N) are a combination of different types of terminalswhere the types of terminals include one or more of: ATMs, assistedcheckout terminals, self-service checkout terminals or kiosks.

An event is an action or occurrence that relates to a terminal or adevice that is part of the terminal. Each event causes event data to becreated that includes information about the event such as the type ofevent. Events typically relate to failures or abnormal actions. An eventcan have multiple severity levels however each event is classified aseither fatal or non-fatal. A fatal event renders the device or terminalunable to function. Fatal events require immediate attention to correctthe failure. A non-fatal event does not render the device or terminalunable to function. A non-fatal event can be a recoverable event or anevent that captures operational parameters such as temperature or fanspeed. A non-fatal event does not require an action by servicepersonnel. Event data is captured in the terminal by the terminal datacollector and transmitted to the terminal data agent 125 as each eventoccurs. The following are examples of non-fatal terminal events.

1) Card Reader—Read Error

2) Card Reader—Write Error

3) Card Reader—Throat Error

4) Currency Dispenser—Pick Error

5) Currency Dispenser—Jam in Presenter

6) Processor Temperature

7) Cooling fan speed

Tally data includes cumulative information about a single parameterrelated to the terminal. For example, a terminal or device will trackthe total time the terminal or device is powered and available in atally. Another tally might track the total number of cycles performed bya device. For example, a card read cycle tally tracks the total numberof cards read by a magnetic stripe card reader and a character counttally tracks the total number of characters printed by a printer. Tallydata is updated locally as tracked parameters change but tally data isonly transmitted to the terminal data agent 125 on a periodic basis. Notall devices or terminals generate tally data. In some embodiments, tallydata is transmitted to the terminal data agent 125 once a week.

The terminal data agent 125 receives event and tally data from theterminal data collectors 115 _(1-N) and transmits the data through anetwork 130, such as the Internet or a virtual private network, to aterminal data agent server 140. In some embodiments, a copy of theterminal data agent 125 executes on each of the terminals 110 _(1-N).

A terminal data loader 145 receives the event and tally data from theterminal data agent server 140. The event and tally data is converted toa common format and loaded into a database 160. The database 160includes a plurality of database tables 150 and a predictive servicerules engine 155 (“rules engine”). The rules engine 155 processes theevent data and in some cases the tally data stored in the database 160to determine if a fault event is predicted by the event data,specifically non-fatal event data. The rules engine 155 then determineswhether to schedule a service call with a service scheduler 165 or anaction with an action scheduler 170 for the terminal having thepredicted fault.

Turning to FIG. 2, there is provided a high-level block diagram of adatabase computer server 200. The server 200 includes a processor 205, avolatile memory 210, a non-volatile memory 220 and a communicationscontroller 215. The non-volatile memory 220 includes an operating system240 and the database 160. The database 160 includes a database program225, the database tables 150 and the rules engine 155. The databaseprogram 225 includes the software necessary to operate the database 160.The processor 205 executes software that includes the operating system240 and the database program 225, which causes the processor 205 tocontrol the computer server 200 and implement the features and functionsof the database 160. The communications controller 215 communicates withexternal devices and computers and is controlled by the processor 205 tosend and receive data. In some embodiments, some or all of thepredictive services software is stored and executed on the server 200.

The database tables 150 include a raw terminal database table 230 and aworking terminal database table 235. The terminal data loader 145creates a record in the raw terminal database table 230 for each eventand tally received from each of the terminals 110 _(1-N). Each recordincludes information that identifies which terminal generated the eventor tally and the event or tally information. When a fatal event isreceived and stored in the raw terminal database table 230, software,not shown, will process the fatal event and generate an immediatereactive service call.

The predictive service rules engine 155 is implemented as a set ofstored procedures stored within the database 160. In some embodiments,the rules engine 155 is implemented as one or more programs external tothe database 160 that operate on data stored in the database 160.

In some embodiments, all software components 135 are all implemented onthe database computer 200. In other embodiments, the software components135 are implemented in a multiple computers that also have a processor,a volatile and a non-volatile memory, a communications controller andprograms stored in one or both of the memories that are executed by theprocessor. When the programs are executed by the processor, they causethe processor to perform the functions associated with the componentsand described herein.

FIG. 3A provides a high-level block diagram illustrating an example ofevent and tally records stored in the raw terminal database table 230.The terminal data loader 145 creates a record in the raw terminaldatabase table 230 for each event and tally received from a terminal.Each record includes: information that identifies the terminal thatgenerated the event or tally, information that identifies the data as anevent or tally, information that identifies the type of event or tally,time and date information related to the event or tally, and anyadditional data that is part of the event or tally received from theterminal.

The example shown in FIG. 3A depicts 6 records 300-325 stored in the rawterminal database table 230 that were received from two terminals 110₁₋₂. From the first terminal 110 ₁, the terminal data loader 145 hasloaded a tally data record₁ 300, two non-fatal event data records₁₋₂305,310 and a fatal event data record₁ 315. From the second terminal 110₂, the terminal data loader 145 has loaded a tally data record₁ 320 anda non-fatal event data record₁ 325.

Approximately every 5 minutes, the rules engine 155 processes the datarecords 300-325 stored in the raw terminal database table 230 andidentifies records 300,305,310,320,325 that meet specific criteria. Theidentified records 300,305,310,320,325, if not already in the workingterminal database table 235, are copied to the working terminal databasetable 235. The rules engine 155 only identifies events that areclassified as non-fatal events and only event types that are required topredict one or more of a plurality of predictable faults. (As usedherein, a predictable fault is a fault that causes a device or terminalto fail and to be unavailable for use.) The rules engine 155 canidentify multiple faults, sometimes for the same device. The rulesengine 155 has rules identifying each fault and the type of event datarequired to predict the fault. One feature of this invention is theability to predict fatal faults using only non-fatal event data.

In some embodiments, the rules engine 155 will identify tally data andmove it to the working terminal database table 235. The rules engine 155uses the tally data with event data to predict a fault.

FIG. 3B provides a high-level block diagram illustrating an example ofrecords stored in the working terminal database table 235. As describedabove, the rules engine 155 identified and copied certain records300,305,310,320,325 from the raw terminal database table 230 to records330-350 in the working terminal database table 235. Only tally and eventdata needed to predict a fault are copied to the working terminaldatabase table 235.

Approximately every 15 minutes, the rules engine 155 processes therecords 330-350 stored in the working terminal database table 235 todetermine if one or more faults can be predicted. The rules engine 155includes a fault rule for each type of fault that can be predicted. Aterminal, or device that is part of the terminal, can experiencedifferent types of faults and the rules engine 155 has fault rules foreach type of fault that can be predicted. Each fault rule includes acomplex fault pattern associated with a specific fault. The faultpattern is based on using non-fatal event data associated with thespecific fault. A fault pattern includes information that defines twoevent patterns. If current data satisfies requirements for both eventpatterns, a fault is predicted. The first event pattern requiresdetermining that during a predetermined period of time, a predeterminednumber of non-fatal events of a certain type have occurred. The secondevent pattern requires determining that the first event pattern has beenrepeated a predetermined number of times. The predetermined parameterscan be different for each fault pattern and are based on historical datafor a similar device and terminal.

For example, the failure of a magnetic stripe card reader in an ATMterminal can be predicted by using non-fatal write error eventsgenerated by the magnetic stripe card reader in the ATM terminal. Thefirst event pattern in the fault pattern used to predict this failurerequires determining that 6 non-fatal write error events occur within a24 hour period. The second event pattern requires that the first eventpattern be repeated 4 times. Put another way, over 4 consecutive 24 hourperiods, the number of non-fatal write error events for the magneticstripe card reader must be 6 or more for each 24 hour period.

A single device can have multiple fault patterns where each faultpattern uses a different event type to predict a fault. For example,another fault pattern for the same magnetic strip card reader is basedon read error events instead of write error events. This fault patternrequires determining that 4 non-fatal read error events occur within a24 hour period and that this pattern is repeated 3 times.

The rules engine 155 processes every fault rule for every fault that canbe predicted every 15 minutes. The processing includes selecting theappropriate records from the working terminal database table 235 foreach fault rule and determining if the actual event data matches thefault patterns in each fault rule being processed. For example, when therules engine 155 is processing a fault rule for a magnetic stripe cardreader in an ATM terminal that uses write error events, the rules engine155 will select, in turn, just records with the write error events foreach magnetic stripe card reader in each ATM terminal and apply theassociated fault patterns to the selected write error events. If a matchis found, the rules engine 155 generates a predicted fault signal forthe magnetic stripe card reader that generated the event data.

The predicted fault signal includes information associated with thepredicted fault such as an fault identifier, a predicted time tofailure, service advice that includes either a service to be performedby service personnel or a part or parts to replace or both, device orterminal identification or both, a type of action to take, and alocation if known. Some of information stored in the predicted faultsignal is taken from the fault rule used to predict the fault and someof the information is taken from the event data used to predict thefault.

In some embodiments, a device has a defined operational life limitparameter. A life limit parameter is designed to predict how long adevice can be expected to operate before it fails from normal operation.Life limit data is usually stored in tally data. For a printer, atypical life limit parameter is based on a maximum number of printedcharacters. Each make and model printer will have a printed characterlife limit value. When the total number of characters printed by theprinter reaches its life limit value, the printer is considered to beworn out and it is typically replaced. However, life limit values areusually set so that statistically a large majority of the devices willfail only after reaching their life limit value. This allows the deviceto be identified and replaced prior to failure. However, since most ofthe devices are replaced prior to failure, statistically a large portionof these devices have additional useful life that is lost.

These embodiments allow these devices to be reliably used beyond theiroperational life limits by combining life limit tally data withcorresponding non-fatal event data to predict an actual failure. Therules engine 155 has additional fault rules where the associated faultpattern includes a third pattern. The third pattern determines if tallydata for a life limit parameter for a device exceeds a predeterminedlife limit value. It should be understood that the tally data is notused to, and does not, predict a fault. The tally data is used only todetermine when to modify predetermined event data thresholds. If thelife limit, as defined in the third pattern, is not exceeded, the firstand second event patterns of the fault pattern are applied as taughtabove. However, if the life limit value is exceeded, the predeterminedparameters used in the first and second event patterns are adjustedlower and the patterns are applied to the event data using the adjustedpredetermined parameters. The predetermined parameters are lowered aftera life limit value is reached because the device is now expected to failsoon and lower indications of a failure are use to predict a fault andgenerate a predicted fault signal.

After the rules engine 155 generates a predicted fault signal,information stored in the predicted fault signal is used to determinewhether to send the predicted fault signal to the service scheduler 165or to the action scheduler 170.

In some cases, it is possible to issue a direct action to the terminalthat is predicted to experience the fault and prevent the fault. This isreferred to as self-healing. An example of an action request is to issuea reset command to a terminal. In another example, an action requestwould cause a terminal to execute a series of commands. If the rulesengine 155 determines that an action request is required, it sends thepredicted fault signal to the action scheduler 170. The action scheduler170 creates an action request and communicates with the terminal dataagent server 140 to deliver the action request to the proper terminalwhere the requested action is executed.

The rules engine 155 will generate a predictive service call requestbased on information in the fault rule. The fault rule includesinformation that identifies a service call type, a first service timeperiod, predictive part service advice data and predictive partreplacement advice data that is included in the predictive service callrequest. The rules engine 155 sends the predictive service call requestto a service scheduler 165 to process.

With reference to FIG. 4, there is provided a graph illustratingdifferent types of service calls. The service scheduler 165 receivesdifferent types of service call requests and schedules service calls.Some of the service requests are reactive in nature. Reactive servicerequests can be generated by automatically detecting a fatal event in acustomer's terminal or by a customer calling in a service call requestafter a terminal fails. A reactive service request causes a reactiveservice call 410 to be scheduled as soon as possible or within theservice window required by a contract with the customer.

The service scheduler 165 also handles predictive service call requests.There are two types of predictive service calls 405. The two types arepredictive next visit 415 and predictive imminent 420. Historical datais used to predict the time to fault which is the time between faultprediction and the actual fault. The time to fault can be in hours, daysor weeks depending on the type of predicted fault. The time to fault isstored in the fault rule as the first service time period and isincluded in the predictive service call request. When the servicescheduler 165 receives a predictive service call request, it will createa predictive imminent service call 420 if the first service time periodis 24 hours or less. If the first service time period is more than 24hours, a predictive next visit service call 425 is created. Creating apredictive next visit service call does not cause an actual service callto a location to be scheduled. Creating a predictive imminent servicecall does cause an actual service call to be scheduled.

The service scheduler 165 maintains different types of records includinga scheduled service call type and a non-scheduled service call type. Ascheduled service call record includes information about a service callthat has been scheduled to occur to address an issue. A non-scheduledservice call record includes the information needed to schedule aservice call however no service call is scheduled when the record iscreated. A non-scheduled service call includes information that willdelay the scheduling of a service call until a future time. When thefuture time is reached, the service scheduler 165 takes the followingactions: the service call information in the non-scheduled service callis used to schedule a service call, a scheduled service call type recordis created, and the non-scheduled service call record is deleted. Apredictive next visit service call is a type of non-scheduled servicecall. Predictive imminent service calls and reactive service calls aretypes of scheduled service calls.

The service scheduler 165 calculates a predicted failure time by addingthe first service time period to the current time and date. This time isstored in both types of the predictive service calls. The servicescheduler 165 will convert a predictive next visit service call to apredictive imminent service call if the current time and the predictedfailure time are less than 24 hours apart.

When the service scheduler 165 receives a reactive service request to alocation, it checks all predictive service calls (non-scheduled servicecalls) to determine if any of the calls are for the same location as thereactive service request. If there are one or more predictive servicecalls for the same location, the one or more predictive calls arecombined with the reactive service call and a single service call isscheduled.

A fault rule also includes information concerning the service to performand/or the part to replace during a predictive service call that resultsfrom a fault predicted by the fault rule. This information is requiredbecause the predicted fault has not occurred and field service personnelmay not be able to determine what service to perform or what part toreplace. This information is included in the service notice aspredictive service advice or predictive parts advice. In someembodiments, when a part is predicted to fail, a replacement part isordered so it is available prior to the service call.

In some embodiments, historical terminal event data is periodicallyupdated with current terminal event data and the fault pattern in eachfault rule is tested against the updated historical terminal event datato determine their predictive accuracy. Their predictive accuracy is thefault pattern's ability to predict the related fault. If the predictiveaccuracy for any fault pattern falls below a minimum accuracy level orbelow a prior tested accuracy, one or more of the predeterminedparameters used in the fault pattern is adjusted and the fault patternis retested. The adjusting of predetermined parameters and retestingcontinues until either the accuracy improves or the possible adjustmentshave been tested. If the accuracy improves, the adjusted predeterminedparameters are used in the fault pattern. If the accuracy does notimprove, a notice is generated to alert personnel responsible foroperating the system. In some embodiments, the updating of terminalevent data and the testing of the fault patterns are performedautomatically without human intervention and in real-time or nearreal-time. In such embodiments, an automatic feedback loop isestablished to constantly update and/or refine the fault patterns.

Turning to FIG. 5A, there is provided a high-level flow chartillustrating a part of a method for predicting a fault. In steps500-505, the terminal data loader 145 constantly receive event data froma terminal 110 ₁ and store the event data in the raw terminal databasetable 230. A poll driven process is depicted to better illustrate theprocess but it could be interrupt driven for better performance. In step500, a check is made to determine if event data is available to receive.The event data is received over a network by the terminal data agentserver 140. If no data is available, control passes back to step 500 tocheck again. If event data is available, control is passed to step 505.In step 505, the event data from the terminal 110 ₁ is received. In step510, the received event data is stored in the raw terminal databasetable 230 and control passes back to step 500. In some embodiments,tally data is also received from the terminal 110 ₁ and stored in theraw terminal database table 230.

FIG. 5B provides a high-level flow chart illustrating a portion of amethod for predicting a fault. Steps 515-550 are performed by the rulesengine 155. In step 515, all non-fatal event data stored in the rawterminal database table 230 and associated with a predictable fault isidentified. There are multiple predictable faults and each fault canhave a plurality of event data associated with it. In step 520, theidentified event data is copied to the working terminal database table235. If one or more of the plurality of event data has already beencopied to the working terminal database table 235, no additional copiesare made. The steps illustrated in FIG. 5B are executed periodically. Insome embodiments, the steps are executed every 5 minutes. Data stored inthe working terminal database 235 table is purged when the data reachesa certain age to improve the performance of the database.

FIG. 5C provides a high-level flow chart illustrating a portion of amethod for predicting a fault. In step 525, a predictable fault isselected. There are a plurality of predictable faults. In step 530,non-fatal event data associated with the selected predictable fault iscompared to a predetermined fault pattern for the selected predictablefault. The predetermined fault pattern is a complex fault pattern thatincludes at least two event patterns that must each be satisfied topredict the fault. The first event pattern requires determining thatduring a predetermined period of time, a predetermined number ofnon-fatal events of a certain type have occurred. The second eventpattern requires determining that the first event pattern is repeated apredetermined number of times. The predetermined values can be differentfor each predictable fault. Each predetermined value is stored in thepredetermined fault pattern for the predictable fault.

In step 535, a fault predicted signal is generated when thepredetermined fault pattern is detected in the event data associatedwith the fault. The fault predicted signal includes information aboutthe fault, the type of service action needed to prevent the fault andadvice or actions to take. In step 540, a next predictable fault isselected if there is one. In step 545, if there is a next predictablefault, control passes to step 530 and if not control passes to step 550where execution ends. Steps 530 through 545 are repeated until allpredictable faults have been processed. Steps 525-550 are executedperiodically by the rules engine 155. In some embodiments, steps 525-550are executed every 15 minutes.

FIG. 6 is a high-level flow chart illustrating an exemplar method forscheduling a predictive service call. A service call involves one ormore trained persons traveling to a location to perform a service onsomething that is in need of service. The type of service performed canvary from performing a specific service to replacing a component. Inthis example, a retail company has a number of terminals such as ATMs,kiosks and point of sale terminals and contracts with a service companyto provide service for the terminals. When a terminal fails, a servicecall is scheduled to fix the cause of the failure. This type of servicecall is a reactive service call because it is a reaction to a failure. Aservice contract with the retail company specifies a service windowduring which time the service company must have a service person on siteto fix the problem after the problem is reported. Another type ofservice call is a predictive service call. A predictive service call isgenerated as a result of predicting a fault that has not yet occurred.Unlike a reactive service call, the scheduling of an actual service callfor a predictive service call can be delayed for period of time. Duringthe delay period, if another terminal fails at the same location as thepredicted fault, the reactive service call is combined with thepredictive service call. This reduces the number of trips that are madeto the location and still prevents the predicted fault from occurring.

In step 600, a predicted fault signal is received for a first terminal.The predicted fault signal includes information about the predictedfault. In step 605, a predicted next visit service call request isgenerated. This type of service call does not cause an actual servicecall to be scheduled but does create a record about the predictiveservice call. In step 610, a reactive service call request is receivedfor a second terminal at the same location as the first terminal. Instep 615, the locations of the first and second terminals are compared.If the locations are the same, control is passed to step 625. If thelocations are not the same, control is passed to step 620. In step 620,a service call is scheduled in response to the reactive service callrequest. In step 625, the predicted next visit service call request iscombined with the reactive service call request. In step 630, a servicecall is scheduled to service the combined service call requests.

FIG. 7 provides a high-level flow chart illustrating an exemplar methodfor scheduling a predicted action. The predicted fault signal includesinformation that specifies whether a predictive service call should beperformed or a predicted action should be performed. In this example, apredicted action is performed as result of a predicted fault. Apredicted action includes one or more commands that are sent to aterminal and executed by the terminal to resolve the predicted fault.

In step 700, a predicted fault signal is received for a first terminal.The predicted fault signal specifies that a predicted action should beperformed. In step 705, a predicted action request is generated inresponse to receiving the predicted fault signal. The predicted actionrequest includes the one or more commands to be executed by the firstterminal. In step 710, the predicted action request is transmitted tothe first terminal. In step 715, the first terminal receives thepredicted action request. In step 720, the first terminal executes theone or more commands included in the received action request.

Although particular reference has been made to an embodiment thatincludes a predicted service database and examples have been providedillustrating the invention in combination with ATM terminals and pointof sale terminals, certain other embodiments, variations, andmodifications are also envisioned within the spirit and scope of thefollowing claims. For example, there are embodiments where the inventionincludes kiosk terminals, airport check in terminals, hotel check interminals, and computers.

We claim:
 1. A method for predicting a fatal fault in a terminal usingnon-fatal event data, the method comprising the steps of: receiving aplurality of event data from the terminal where the plurality of eventdata includes non-fatal event data; identifying non-fatal event dataassociated with a fault from the plurality of event data; comparing theidentified non-fatal event data to a predetermined fault patternassociated with the fault; and generating a fault predicted signal forthe terminal when the predetermined fault pattern is found within thenon-fatal event data. 2) The method of claim 1, further comprising:receiving historical event data associated with the fault from aplurality of terminals where the historical event data includes fataland non-fatal event data; and processing the historical event data toidentify the predetermined fault pattern associated with the fault wherethe identified fault pattern is based on non-fatal event data. 3) Themethod of claim 1 further comprising: receiving configuration data forthe plurality of terminals; and refining the identified predeterminedfault pattern based on the configuration data. 4) The method of claim 1,further comprising: repeating the receiving, identifying and comparingsteps when the predetermined fault pattern is not identified within thenon-fatal event data. 5) The method of claim 1, wherein thepredetermined fault pattern includes a first pattern wherein during afirst predetermined period of time the number of events exceeds a firstthreshold and wherein the predetermined fault pattern includes a secondpattern wherein the first pattern is repeated a predetermined number oftimes. 6) The method of claim 1, wherein the event data includes anenvironmental parameter. 7) The method of claim 6, wherein theenvironmental parameter is a temperature. 8) The method of claim 1,wherein the event data include an operational parameter. 9) The methodof claim 8, wherein the operational parameter is a recoverable magneticstripe card operation. 10) The method of claim 8, wherein theoperational parameter is an RPM value for a fan. 11) The method of claim1, further comprising: receiving tally data from the terminal; andadjusting the predetermined fault pattern when the tally data associatedwith the fault exceeds a predetermined threshold for the tally datawhere the predetermined threshold for the tally data is a life limitvalue. 12) The method of claim 11, wherein adjusting the predeterminedfault pattern includes adjusting at least one predetermined value in thepredetermined fault pattern lower. 13) The method of claim 11, whereinthe tally data is a total operating time. 14) The method of claim 11,wherein the tally data is a total cycle count. 15) The method of claim1, further comprising: capturing event data at the terminal; andtransferring the event data from the terminal. 16) The method of claim1, wherein the terminal is an automatic teller machine. 17) The methodof claim 1, wherein the terminal is a point of sale terminal. 18) Themethod of claim 1, further comprising: generating a predictive servicenext visit call request to a first location where the terminal islocated in response to the predicted fault signal wherein the predictiveservice next visit call request includes a first service time periodduring which a predictive service call associated with the request isnot scheduled. 19) The method of claim 18, wherein a reactive servicecall is scheduled to the first location during the first service timeperiod and the predictive service next visit call is combined with thereactive service call. 20) The method of claim 18, wherein generatingthe predictive service next visit call request includes predictive partservice advice. 21) The method of claim 18, wherein generating thepredictive service next visit call request includes predictive partsreplacement advice. 22) The method of claim 1, further comprising:generating a predictive action request for the terminal in response tothe predicted fault signal. 23) A fault predicting system for predictinga fault in a terminal, the system comprising: a computer memory whereina plurality of computer programs and data are stored; and a computerprocessor in communication with the computer memory wherein when thecomputer programs are executed by the computer processor operations areperformed, the operations comprising: receiving a plurality of eventdata from the terminal where the plurality of event data includesnon-fatal event data; identifying non-fatal event data associated with afault from the plurality of event data; comparing the identifiednon-fatal event data to a predetermined fault pattern associated withthe fault; and generating a fault predicted signal for the terminal whenthe predetermined fault pattern is found within the non-fatal eventdata. 24) The system of claim 23, the operations further comprising:receiving historical event data associated with the fault from aplurality of terminals where the historical event data includes fataland non-fatal event data; and processing the historical event data toidentify the predetermined fault pattern associated with the fault wherethe identified fault pattern is based on non-fatal event data. 25) Thesystem of claim 23, the operations further comprising: determining thatthe predictive failure date is less than 24 hours away and convertingthe predictive service next visit call request to a predictive imminentservice request; and scheduling a predictive service call in response tothe predictive imminent service request. 26) The system of claim 23, theoperations further comprising: receiving configuration data for theplurality of terminals; and refining the identified predetermined faultpattern based on the configuration data. 27) The system of claim 23, theoperations further comprising: repeating the receiving, identifying andcomparing steps when the predetermined fault pattern is not identifiedwithin the non-fatal event data. 28) The system of claim 23, wherein thepredetermined fault pattern includes a first pattern wherein during afirst predetermined period of time the number of events exceeds a firstthreshold and wherein the predetermined fault pattern includes a secondpattern wherein the first pattern is repeated a predetermined number oftimes.